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@article{180279,
        author = {Ms. Ritu Jaiswal and Dr. Ranjana Rajnish},
        title = {AI application for crop management and yield prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1049-1053},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180279},
        abstract = {Agriculture, practiced on 60.45% of India’s land, has evolved with AI technologies like ML, computer vision, and big data to improve crop management and yield prediction. Tools such as satellite imagery, drones, and IoT devices monitor soil, weather, and pests. ML algorithms process this data to assess risks and suggest actions, with CNNs and Random Forests aiding in disease detection and yield prediction [1].These advancements boost productivity and sustainability by reducing chemical use and conserving water. However, challenges like limited data and unequal tech access remain. This study aims to identify suitable crops based on soil type, humidity, and temperature, and forecast yield.},
        keywords = {AI, ML, Random Forest, SVM, Decision Tree, KNN},
        month = {June},
        }